Although tremendous achievements have been made in person re-ID [1]}, [2]}, there still remains a gap to apply re-ID to practical applications, due to that gallery images are cropped person images instead of natural images consisting of persons and their surroundings. To address this, person search is introduced to jointly localize and identify query persons. Existing person search frameworks can be generally divided into two categories: two-step and one-step models. In two-step frameworks, the person detection results are fed into a re-ID model, i.e., detection and re-ID models are independent of each other and organized in a sequential way. Inspired by the achievements in object detection [3]}, the first one-step person search approach based on Faster-RCNN [3]} was proposed in [5]}. Since then, many more successful attempts [6]}, [7]}, [8]}, [9]}, [10]}, [11]} have been made to make the joint framework more effective and efficient. For instance, to improve the efficiency, Yan et al. [7]} proposed the first anchor-free person search framework, which equipped an existing detector with three levels of alignment. In [8]}, detection and re-ID were considered as a progressive process and tackled with two sub-networks. However, existing person search models suffer a lot from the conflicting objects of person detection and re-ID during training [14]}. To tackle this problem, Chen et al. proposed a novel norm-aware embedding (NAE) method to decompose the feature embedding into norm and angle for detection and re-ID, respectively.
